# import math
# from collections import namedtuple
#
# import torch
#
# from modules import prompt_parser, devices, sd_hijack, sd_emphasis
# from modules.shared import opts
#
#
# class PromptChunk:
#     """
#     This object contains token ids, weight (multipliers:1.4) and textual inversion embedding info for a chunk of prompt.
#     If a prompt is short, it is represented by one PromptChunk, otherwise, multiple are necessary.
#     Each PromptChunk contains an exact amount of tokens - 77, which includes one for start and end token,
#     so just 75 tokens from prompt.
#     """
#
#     def __init__(self):
#         self.tokens = []
#         self.multipliers = []
#         self.fixes = []
#
#
# PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])
# """An object of this type is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt
# chunk. Those objects are found in PromptChunk.fixes and, are placed into FrozenCLIPEmbedderWithCustomWordsBase.hijack.fixes, and finally
# are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
#
#
# class TextConditionalModel(torch.nn.Module):
#     def __init__(self):
#         super().__init__()
#
#         self.hijack = sd_hijack.model_hijack
#         self.chunk_length = 75
#
#         self.is_trainable = False
#         self.input_key = 'txt'
#         self.return_pooled = False
#
#         self.comma_token = None
#         self.id_start = None
#         self.id_end = None
#         self.id_pad = None
#
#     def empty_chunk(self):
#         """creates an empty PromptChunk and returns it"""
#
#         chunk = PromptChunk()
#         chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1)
#         chunk.multipliers = [1.0] * (self.chunk_length + 2)
#         return chunk
#
#     def get_target_prompt_token_count(self, token_count):
#         """returns the maximum number of tokens a prompt of a known length can have before it requires one more PromptChunk to be represented"""
#
#         return math.ceil(max(token_count, 1) / self.chunk_length) * self.chunk_length
#
#     def tokenize(self, texts):
#         """Converts a batch of texts into a batch of token ids"""
#
#         raise NotImplementedError
#
#     def encode_with_transformers(self, tokens):
#         """
#         converts a batch of token ids (in python lists) into a single tensor with numeric representation of those tokens;
#         All python lists with tokens are assumed to have same length, usually 77.
#         if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on
#         model - can be 768 and 1024.
#         Among other things, this call will read self.hijack.fixes, apply it to its inputs, and clear it (setting it to None).
#         """
#
#         raise NotImplementedError
#
#     def encode_embedding_init_text(self, init_text, nvpt):
#         """Converts text into a tensor with this text's tokens' embeddings. Note that those are embeddings before they are passed through
#         transformers. nvpt is used as a maximum length in tokens. If text produces less teokens than nvpt, only this many is returned."""
#
#         raise NotImplementedError
#
#     def tokenize_line(self, line):
#         """
#         this transforms a single prompt into a list of PromptChunk objects - as many as needed to
#         represent the prompt.
#         Returns the list and the total number of tokens in the prompt.
#         """
#
#         if opts.emphasis != "None":
#             parsed = prompt_parser.parse_prompt_attention(line)
#         else:
#             parsed = [[line, 1.0]]
#
#         tokenized = self.tokenize([text for text, _ in parsed])
#
#         chunks = []
#         chunk = PromptChunk()
#         token_count = 0
#         last_comma = -1
#
#         def next_chunk(is_last=False):
#             """puts current chunk into the list of results and produces the next one - empty;
#             if is_last is true, tokens <end-of-text> tokens at the end won't add to token_count"""
#             nonlocal token_count
#             nonlocal last_comma
#             nonlocal chunk
#
#             if is_last:
#                 token_count += len(chunk.tokens)
#             else:
#                 token_count += self.chunk_length
#
#             to_add = self.chunk_length - len(chunk.tokens)
#             if to_add > 0:
#                 chunk.tokens += [self.id_end] * to_add
#                 chunk.multipliers += [1.0] * to_add
#
#             chunk.tokens = [self.id_start] + chunk.tokens + [self.id_end]
#             chunk.multipliers = [1.0] + chunk.multipliers + [1.0]
#
#             last_comma = -1
#             chunks.append(chunk)
#             chunk = PromptChunk()
#
#         for tokens, (text, weight) in zip(tokenized, parsed):
#             if text == 'BREAK' and weight == -1:
#                 next_chunk()
#                 continue
#
#             position = 0
#             while position < len(tokens):
#                 token = tokens[position]
#
#                 if token == self.comma_token:
#                     last_comma = len(chunk.tokens)
#
#                 # this is when we are at the end of allotted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack
#                 # is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next.
#                 elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.comma_padding_backtrack:
#                     break_location = last_comma + 1
#
#                     reloc_tokens = chunk.tokens[break_location:]
#                     reloc_mults = chunk.multipliers[break_location:]
#
#                     chunk.tokens = chunk.tokens[:break_location]
#                     chunk.multipliers = chunk.multipliers[:break_location]
#
#                     next_chunk()
#                     chunk.tokens = reloc_tokens
#                     chunk.multipliers = reloc_mults
#
#                 if len(chunk.tokens) == self.chunk_length:
#                     next_chunk()
#
#                 embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, position)
#                 if embedding is None:
#                     chunk.tokens.append(token)
#                     chunk.multipliers.append(weight)
#                     position += 1
#                     continue
#
#                 emb_len = int(embedding.vectors)
#                 if len(chunk.tokens) + emb_len > self.chunk_length:
#                     next_chunk()
#
#                 chunk.fixes.append(PromptChunkFix(len(chunk.tokens), embedding))
#
#                 chunk.tokens += [0] * emb_len
#                 chunk.multipliers += [weight] * emb_len
#                 position += embedding_length_in_tokens
#
#         if chunk.tokens or not chunks:
#             next_chunk(is_last=True)
#
#         return chunks, token_count
#
#     def process_texts(self, texts):
#         """
#         Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum
#         length, in tokens, of all texts.
#         """
#
#         token_count = 0
#
#         cache = {}
#         batch_chunks = []
#         for line in texts:
#             if line in cache:
#                 chunks = cache[line]
#             else:
#                 chunks, current_token_count = self.tokenize_line(line)
#                 token_count = max(current_token_count, token_count)
#
#                 cache[line] = chunks
#
#             batch_chunks.append(chunks)
#
#         return batch_chunks, token_count
#
#     def forward(self, texts):
#         """
#         Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts.
#         Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will
#         be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, for SD2 it's 1024, and for SDXL it's 1280.
#         An example shape returned by this function can be: (2, 77, 768).
#         For SDXL, instead of returning one tensor avobe, it returns a tuple with two: the other one with shape (B, 1280) with pooled values.
#         Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one element
#         is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
#         """
#
#         batch_chunks, token_count = self.process_texts(texts)
#
#         used_embeddings = {}
#         chunk_count = max([len(x) for x in batch_chunks])
#
#         zs = []
#         for i in range(chunk_count):
#             batch_chunk = [chunks[i] if i < len(chunks) else self.empty_chunk() for chunks in batch_chunks]
#
#             tokens = [x.tokens for x in batch_chunk]
#             multipliers = [x.multipliers for x in batch_chunk]
#             self.hijack.fixes = [x.fixes for x in batch_chunk]
#
#             for fixes in self.hijack.fixes:
#                 for _position, embedding in fixes:
#                     used_embeddings[embedding.name] = embedding
#             devices.torch_npu_set_device()
#             z = self.process_tokens(tokens, multipliers)
#             zs.append(z)
#
#         if opts.textual_inversion_add_hashes_to_infotext and used_embeddings:
#             hashes = []
#             for name, embedding in used_embeddings.items():
#                 shorthash = embedding.shorthash
#                 if not shorthash:
#                     continue
#
#                 name = name.replace(":", "").replace(",", "")
#                 hashes.append(f"{name}: {shorthash}")
#
#             if hashes:
#                 if self.hijack.extra_generation_params.get("TI hashes"):
#                     hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
#                 self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
#
#         if any(x for x in texts if "(" in x or "[" in x) and opts.emphasis != "Original":
#             self.hijack.extra_generation_params["Emphasis"] = opts.emphasis
#
#         if self.return_pooled:
#             return torch.hstack(zs), zs[0].pooled
#         else:
#             return torch.hstack(zs)
#
#     def process_tokens(self, remade_batch_tokens, batch_multipliers):
#         """
#         sends one single prompt chunk to be encoded by transformers neural network.
#         remade_batch_tokens is a batch of tokens - a list, where every element is a list of tokens; usually
#         there are exactly 77 tokens in the list. batch_multipliers is the same but for multipliers instead of tokens.
#         Multipliers are used to give more or less weight to the outputs of transformers network. Each multiplier
#         corresponds to one token.
#         """
#         tokens = torch.asarray(remade_batch_tokens).to(devices.device)
#
#         # this is for SD2: SD1 uses the same token for padding and end of text, while SD2 uses different ones.
#         if self.id_end != self.id_pad:
#             for batch_pos in range(len(remade_batch_tokens)):
#                 index = remade_batch_tokens[batch_pos].index(self.id_end)
#                 tokens[batch_pos, index+1:tokens.shape[1]] = self.id_pad
#
#         z = self.encode_with_transformers(tokens)
#
#         pooled = getattr(z, 'pooled', None)
#
#         emphasis = sd_emphasis.get_current_option(opts.emphasis)()
#         emphasis.tokens = remade_batch_tokens
#         emphasis.multipliers = torch.asarray(batch_multipliers).to(devices.device)
#         emphasis.z = z
#
#         emphasis.after_transformers()
#
#         z = emphasis.z
#
#         if pooled is not None:
#             z.pooled = pooled
#
#         return z
#
#
# class FrozenCLIPEmbedderWithCustomWordsBase(TextConditionalModel):
#     """A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
#     have unlimited prompt length and assign weights to tokens in prompt.
#     """
#
#     def __init__(self, wrapped, hijack):
#         super().__init__()
#
#         self.hijack = hijack
#
#         self.wrapped = wrapped
#         """Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
#         depending on model."""
#
#         self.is_trainable = getattr(wrapped, 'is_trainable', False)
#         self.input_key = getattr(wrapped, 'input_key', 'txt')
#         self.return_pooled = getattr(self.wrapped, 'return_pooled', False)
#
#         self.legacy_ucg_val = None  # for sgm codebase
#
#     def forward(self, texts):
#         if opts.use_old_emphasis_implementation:
#             import modules.sd_hijack_clip_old
#             return modules.sd_hijack_clip_old.forward_old(self, texts)
#
#         return super().forward(texts)
#
#
# class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
#     def __init__(self, wrapped, hijack):
#         super().__init__(wrapped, hijack)
#         self.tokenizer = wrapped.tokenizer
#
#         vocab = self.tokenizer.get_vocab()
#
#         self.comma_token = vocab.get(',</w>', None)
#
#         self.token_mults = {}
#         tokens_with_parens = [(k, v) for k, v in vocab.items() if '(' in k or ')' in k or '[' in k or ']' in k]
#         for text, ident in tokens_with_parens:
#             mult = 1.0
#             for c in text:
#                 if c == '[':
#                     mult /= 1.1
#                 if c == ']':
#                     mult *= 1.1
#                 if c == '(':
#                     mult *= 1.1
#                 if c == ')':
#                     mult /= 1.1
#
#             if mult != 1.0:
#                 self.token_mults[ident] = mult
#
#         self.id_start = self.wrapped.tokenizer.bos_token_id
#         self.id_end = self.wrapped.tokenizer.eos_token_id
#         self.id_pad = self.id_end
#
#     def tokenize(self, texts):
#         tokenized = self.wrapped.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
#
#         return tokenized
#
#     def encode_with_transformers(self, tokens):
#         outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
#
#         if opts.CLIP_stop_at_last_layers > 1:
#             z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
#             z = self.wrapped.transformer.text_model.final_layer_norm(z)
#         else:
#             z = outputs.last_hidden_state
#
#         return z
#
#     def encode_embedding_init_text(self, init_text, nvpt):
#         embedding_layer = self.wrapped.transformer.text_model.embeddings
#         ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
#         embedded = embedding_layer.token_embedding.wrapped(ids.to(embedding_layer.token_embedding.wrapped.weight.device)).squeeze(0)
#
#         return embedded
#
#
# class FrozenCLIPEmbedderForSDXLWithCustomWords(FrozenCLIPEmbedderWithCustomWords):
#     def __init__(self, wrapped, hijack):
#         super().__init__(wrapped, hijack)
#
#     def encode_with_transformers(self, tokens):
#         outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=self.wrapped.layer == "hidden")
#
#         if opts.sdxl_clip_l_skip is True:
#             z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
#         elif self.wrapped.layer == "last":
#             z = outputs.last_hidden_state
#         else:
#             z = outputs.hidden_states[self.wrapped.layer_idx]
#
#         return z
